Analysis of merged questionnaires
Anna Quaglieri & Riccardo Amorati
03/09/2017
library(readr)
library(tidyverse)
library(reshape2)
library(corrplot)
library(psych)
library(pheatmap)
library(RColorBrewer)
library(cowplot)
# Chunk options
knitr::opts_chunk$set(echo = TRUE, prompt = TRUE,cache = TRUE)Read in data
> european <- read_csv("european_recoded.csv")
> australian <- read_csv("australian_recoded.csv")
>
> all <- merge(european,australian,all = TRUE)Variables that are interesting but are different between contexts
> demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
> l2School <- "\\.L2school$"
> l2School_variables <- colnames(all)[grep(l2School,colnames(all))]- First language
> #table(all$L1,all$Context) # too many levels - needs to be cleaned (ex tot number of languages?)
> table(all$L1,useNA = "always")##
## Afrikaans Albanian Burmese
## 1 2 1
## Cantonese Chinese Croatian
## 4 7 1
## Dutch English English and Dutch
## 1 201 2
## German German and English German and Turkish
## 77 2 1
## I Indonesian Italian
## 1 1 89
## Japanese Mandarin Persian
## 1 5 1
## Persian (Farsi) Romanian Russian
## 1 3 2
## Sindhi Slovak Spanish
## 1 1 3
## Turkish Ukrainian <NA>
## 1 1 67> ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="")
> #table(all$speak.other.L2,all$Context)> L2 <- data.frame(Freq=table(all$speak.other.L2)[order(table(all$speak.other.L2),decreasing = TRUE)],
+ L2=names(table(all$speak.other.L2))[order(table(all$speak.other.L2),decreasing = TRUE)]) # too many levels - needs to be cleaned (ex tot number of languages?)
> head(L2)## Freq.Var1 Freq.Freq L2
## 1 No 171 No
## 2 Yes 143 Yes
## 3 French 13 French
## 4 English 9 English
## 5 Italian 7 Italian
## 6 Japanese 4 Japanese- origins
> table(all$origins,useNA = "always")##
## No Yes <NA>
## 323 89 66> table(all$year.studyL2)##
## 0 years 1- 3 years
## 69 12
## 1-3 years 4-6 years
## 11 61
## First year of primary school Kindergarten
## 80 30
## Less than a year more than 6 years
## 27 49
## Other
## 72> table(all$degree)##
## BA in Anglistik BA in Nordamerikastudien
## 43 4
## HUM HUM.SCI
## 129 6
## LA Lingue e letterature straniere
## 36 82
## Lingue, mercati e culture dell'Asia QC
## 13 5
## SCI
## 86- study.year in the European context is uni1.year in the Australian context
> all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
> #table(all$study.year)
> all$study.year <- ifelse(all$study.year == "Already graduated after 5 semesters in March 2016, was interested in survery/study, sorry.","6th semester",all$study.year)
> table(all$study.year)##
## 1st semester 1st year 2nd semester
## 72 255 5
## 2nd year 3rd semester 3rd year
## 37 5 20
## 3rd year of Master 4th year bachelor 5th semester
## 1 8 2
## 6th semester 7th year Master
## 3 1 3- proficiency
> table(all$prof,useNA = "always")##
## Advanced Elementary Intermediate
## 78 105 84
## Upper-intermediate <NA>
## 146 65- Barplot filtered
Filter participants : keep only the ones that meet the inclusion criteria.
> all$study.year[is.na(all$study.year)] <- all$uni1.year[is.na(all$study.year)]
> # Filter only subject that we want to include in the study
> # names(table(all$study.year))[1] = 1st semester"
>
> filtered <- subset(all, (study.year == "1st year") | (study.year == names(table(all$study.year))[1]) & year.studyL2 != "0 years")
>
> table(filtered$Context)##
## English in Germany English in Italy German in Australia
## 71 91 89
## Italian in Australia
## 75> table(all$Context)##
## English in Germany English in Italy German in Australia
## 96 113 146
## Italian in Australia
## 123Demographics
> all <- filtered
> demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
> l2School <- "\\.L2school$"
> l2School_variables <- colnames(all)[grep(l2School,colnames(all))]
>
> ggplot(all,aes(x=L1,fill=Context)) + geom_bar() + coord_flip() + ggtitle("First Language") + labs(y="N. of participants",x="") + theme_bw()
> table(all$L1,all$Context)##
## English in Germany English in Italy
## Afrikaans 0 0
## Albanian 0 1
## Cantonese 0 0
## Chinese 0 2
## Dutch 1 0
## English 1 0
## English and Dutch 0 0
## German 64 0
## German and English 1 0
## I 0 0
## Indonesian 0 0
## Italian 0 87
## Japanese 0 0
## Mandarin 0 0
## Persian (Farsi) 0 0
## Romanian 0 0
## Russian 2 0
## Sindhi 0 0
## Spanish 1 0
## Turkish 1 0
## Ukrainian 0 1
##
## German in Australia Italian in Australia
## Afrikaans 1 0
## Albanian 0 0
## Cantonese 2 0
## Chinese 2 0
## Dutch 0 0
## English 75 73
## English and Dutch 2 0
## German 0 0
## German and English 1 0
## I 0 1
## Indonesian 1 0
## Italian 0 0
## Japanese 1 0
## Mandarin 1 1
## Persian (Farsi) 1 0
## Romanian 1 0
## Russian 0 0
## Sindhi 1 0
## Spanish 0 0
## Turkish 0 0
## Ukrainian 0 0- Filter for L1
> #Filter by L1
>
> nc <- names(table(all$Context))
> l1_filter <- all[(all$Context == nc[1] & (all$L1 == "German" | all$L1 == "German and English")) |
+ (all$Context == nc[2] & (all$L1 == "Italian")) |
+ (all$Context == nc[3] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")) |
+ (all$Context == nc[4] & (all$L1 == "English" | all$L1 == "English and Dutch" | all$L1 == "German and English")),]
>
>
> all <- l1_filter
>
> # subset demographics
> demo <- subset(all,select=c("Resp.ID",demographics_var,l2School_variables))
>
> # Numeri finali
> table(l1_filter$Context)##
## English in Germany English in Italy German in Australia
## 65 87 78
## Italian in Australia
## 73- Filter missing value:
- Filter participants who didn’t put the degree
- we don’t care about speak.other.L2 and study.other.L2
> missing_bySample <- rowSums(is.na(demo))
> names(missing_bySample) <- demo$Resp.ID
> missing_byVar <- colSums(is.na(demo))
> names(missing_byVar) <- colnames(demo)
>
> barplot(missing_bySample)
> d <- data.frame(miss=missing_byVar)
> d$varID <- rownames(d)
> ggplot(data=d,aes(x=varID,y=miss)) + geom_bar(stat="identity") + theme_bw() +theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
> demo_missing <- demo %>% group_by(Context) %>% summarise(roleL2.degree_na = sum(is.na(roleL2.degree)),
+ L2.VCE_na = sum(is.na(L2.VCE)),
+ other5.other.ways_na=sum(is.na(other5.other.ways )),
+ uni1.year_na = sum(is.na(uni1.year)),
+ primary1.L2school_na=sum(is.na(primary1.L2school)),
+ CLS3.L2school_na = sum(is.na(CLS3.L2school)),
+ VSL4.L2school_na=sum(is.na(VSL4.L2school)),
+ degree = sum(is.na(degree)),
+ schooL2country5.L2school_na=sum(is.na(schooL2country5.L2school)))
>
> # We do not filter for speak.other.L2 or study.other.L2
>
> #demo[is.na(demo$speak.other.L2),]
> # teniamo
> #demo[is.na(demo$study.other.L2),]
> missing_bySample[names(missing_bySample) == "5166861581"]## 5166861581
## 10> #demo[is.na(demo$year.studyL2),]
> missing_bySample[names(missing_bySample) == "5378798787"]## 5378798787
## 3> # remove NA from degree
> #table(demo$degree,useNA = "always")
> # Remove people
> all <- all[!is.na(all$degree),]
>
>
> table(all$Context)##
## English in Germany English in Italy German in Australia
## 64 87 77
## Italian in Australia
## 72Summary demographics
to change yearL2.study.richi
> # add numbers on the bar
> ggplot(all,aes(x=Age,fill=Context)) + geom_bar(position="dodge",colour="white") + theme_bw() + labs(y="N participants")
> # add numbers on the bar
> ggplot(all,aes(x=origins,fill=Context)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Origins by context")
> # year study L2
> table(all$year.studyL2,all$other.year.studyL2.richi)##
## BILINGUAL FIRST.YEAR.SECONDARY
## 0 years 0 0
## 1- 3 years 0 0
## 1-3 years 0 0
## 4-6 years 0 0
## First year of primary school 0 0
## Kindergarten 0 0
## Less than a year 0 0
## more than 6 years 0 0
## Other 3 7
##
## FOURTH YEAR PRIMARY FOURTH.YEAR.PRIMARY
## 0 years 0 0
## 1- 3 years 0 0
## 1-3 years 0 0
## 4-6 years 0 0
## First year of primary school 0 0
## Kindergarten 0 0
## Less than a year 0 0
## more than 6 years 0 0
## Other 1 3
##
## LOWER.SECONDARY PERSONAL
## 0 years 0 0
## 1- 3 years 0 0
## 1-3 years 0 0
## 4-6 years 0 0
## First year of primary school 0 0
## Kindergarten 0 0
## Less than a year 0 0
## more than 6 years 0 0
## Other 4 2
##
## SECOND.YEAR.ELEMENTARY SECOND.YEAR.PRIMARY
## 0 years 0 0
## 1- 3 years 0 0
## 1-3 years 0 0
## 4-6 years 0 0
## First year of primary school 0 0
## Kindergarten 0 0
## Less than a year 0 0
## more than 6 years 0 0
## Other 1 1
##
## SECOND.YEAR.SECONDARY THIRD.YEAR.PRIMARY
## 0 years 0 0
## 1- 3 years 0 0
## 1-3 years 0 0
## 4-6 years 0 0
## First year of primary school 0 0
## Kindergarten 0 0
## Less than a year 0 0
## more than 6 years 0 0
## Other 2 27> all$year.studyL2 <- ifelse(all$year.studyL2 == "Other",all$other.year.studyL2.richi,all$year.studyL2 )
>
> # European context
> ggplot(all[all$Context == "English in Germany" | all$Context == "English in Italy",],aes(x=degree,fill=year.studyL2)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year L2, by Context") + facet_grid(~Context,scales="free") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(y = "N participants", x = "degree")
> # Australian context
> ggplot(all[all$Context == "Italian in Australia" | all$Context == "German in Australia",],aes(x=degree,fill=study.year)) + geom_bar(position="dodge",colour="white") + theme_bw() + ggtitle("Degree by study year, by previous study") + coord_flip() + facet_grid(Context~year.studyL2,scales="free")
> all_melt <- melt(all,id.vars = c("Resp.ID","Gender","Age","prof","Context","study.year"),
+ measure.vars = likert_variables_all)
>
> all_melt$value <- factor(all_melt$value,levels=c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
>
> all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)## Warning: Too few values at 600 locations: 8701, 8702, 8703, 8704, 8705,
## 8706, 8707, 8708, 8709, 8710, 8711, 8712, 8713, 8714, 8715, 8716, 8717,
## 8718, 8719, 8720, ...> ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack") +
+ facet_grid(Context~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8)) + ggtitle("Filtered dataset")
> filt_sum <- all_melt %>% group_by(Context,variable,type,value) %>% dplyr::summarise(Ngroup=length(value))
> ggplot(filt_sum,aes(x=value,y=Ngroup,colour=Context,group=interaction(variable, Context))) + geom_line() + geom_point() + facet_wrap(~type,scales = "free")+theme(axis.text.x = element_text(angle = 45, hjust = 1))
Convert Likert scales to numbers
- correlation plot of items by context
> convertToNumber <- function(column){
+ column <- factor(column,levels = c("Strongly disagree","Disagree","Not sure","Agree","Strongly agree"))
+ column_number <- as.numeric(column)
+ return(column_number)
+ }
>
> table(all$Context)##
## English in Germany English in Italy German in Australia
## 64 87 77
## Italian in Australia
## 72> convert_likert <- data.frame(apply(subset(all,select=likert_variables_all),2,convertToNumber))
> colnames(convert_likert) <- paste0(colnames(convert_likert),"1")
>
> likert_variables1 <- paste0(likert_variables_all,"1")
>
> # join the converted variables to the filtered dataset
> filtered_conv <- cbind(all,convert_likert)
>
> table(filtered_conv[,likert_variables_all[1]],filtered_conv[,likert_variables1[1]],useNA = "always")##
## 2 3 4 5 <NA>
## Agree 0 0 120 0 0
## Disagree 8 0 0 0 0
## Not sure 0 27 0 0 0
## Strongly agree 0 0 0 145 0
## <NA> 0 0 0 0 0- Italian in Australia
> cov <- cor(filtered_conv[filtered_conv$Context == "Italian in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
> #corrplot(cov,method="color",is.corr = TRUE,order = "hclust",tl.col = "black",mar = c(1, 0,1, 0),tl.cex=0.8)
>
> library(pheatmap)
> row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
> row_infos$Variables <- as.character(row_infos$Variables)
> rownames(row_infos) <- rownames(cov)
> row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
> row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
>
> ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
> ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
>
> #pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE], annotation_colors = ann_colors_wide,breaks=seq(-1,1,0.2),col=c("#67001f","#b2182b","#d6604d","#f4a582","#fddbc7","#f7f7f7","#d1e5f0","#92c5de","#4393c3","#2166ac","#053061"),show_colnames = FALSE,width = 7,height = 7)
> ###################
>
> diag(cov) <- NA
> pheatmap(cov, main = "Italian in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
+ , annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
- German in Australia
> cov <- cor(filtered_conv[filtered_conv$Context == "German in Australia",likert_variables1[!(likert_variables1 %in% "necessity1")]],method = "pearson",use="pairwise.complete.obs")
>
> row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
> row_infos$Variables <- as.character(row_infos$Variables)
> rownames(row_infos) <- rownames(cov)
> row_infos$Variables[which(is.na(row_infos$Variables))] <- c("educated")
> row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
>
> ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
> ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
>
> diag(cov) <- NA
> pheatmap(cov, main = "German in Australia",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
+ , annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
- English in Germany
> cov <- cor(filtered_conv[filtered_conv$Context == "English in Germany",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1", "speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
>
> row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
> row_infos$Variables <- as.character(row_infos$Variables)
> rownames(row_infos) <- rownames(cov)
> row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity")
> row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
>
> ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
> ann_colors_wide <- list(Variables=c(id1="#f6e8c3",necessity="#b35806",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
>
> diag(cov) <- NA
> pheatmap(cov, main = "English in Germany",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
+ , annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
- English in Italy
> cov <- cor(filtered_conv[filtered_conv$Context == "English in Italy",likert_variables1[!(likert_variables1 %in% c("reconnect.comm1","speakersmelb.comm1","comecloser.comm1","educated1"))]],method = "pearson",use="pairwise.complete.obs")
>
> row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
> row_infos$Variables <- as.character(row_infos$Variables)
> rownames(row_infos) <- rownames(cov)
> row_infos$Variables[which(is.na(row_infos$Variables))] <- "necessity"
> row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
>
> ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
> ann_colors_wide <- list(Variables=c(comm1="#bd0026",necessity="#b35806", id1="#f6e8c3",instru1="#35978f",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
>
> diag(cov) <- NA
> pheatmap(cov, main = "English in Italy",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
+ , annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
> cov <- cor(filtered_conv[,likert_variables1],method = "pearson",use="pairwise.complete.obs")
>
> row_infos <- data.frame(Variables=sapply(strsplit(colnames(cov),split="\\."),function(x) x[2]))
> row_infos$Variables <- as.character(row_infos$Variables)
> rownames(row_infos) <- rownames(cov)
> row_infos$Variables[which(is.na(row_infos$Variables))] <- c("necessity","educated")
> row_infos <- row_infos[order(row_infos$Variables),,drop=FALSE]
>
> ann_col_wide <- data.frame(Variable=unique(row_infos$Variables))
> ann_colors_wide <- list(Variables=c(comm1="#bd0026",educated="orange", id1="#f6e8c3",instru1="#35978f",necessity="#b35806",integr1="#386cb0",intr1="#ffff99",ought1="grey",post1="black",prof1="pink"))
>
> diag(cov) <- NA
> pheatmap(cov, main = "All Contexts",annotation_names_row = FALSE,cluster_cols=TRUE,cluster_rows=TRUE,annotation_col = row_infos[,1,drop=FALSE], annotation_row = row_infos[,1,drop=FALSE]
+ , annotation_colors = ann_colors_wide,show_colnames = FALSE,breaks = seq(-0.6,0.7,length.out = 50),width = 7,height = 7,color=colorRampPalette(brewer.pal(n = 7, name = "RdBu"))(50))
Evaluate internal constiency of known constructs
> sets <- list(id.var=likert_variables1[grep("\\.id1$",likert_variables1)],
+ ought.var=likert_variables1[grep("\\.ought1$",likert_variables1)],
+ intr.var=likert_variables1[grep("\\.intr1$",likert_variables1)],
+ instru.var=likert_variables1[grep("\\.instru1$",likert_variables1)],
+ integr1.var=likert_variables1[grep("\\.integr1$",likert_variables1)],
+ prof.var=likert_variables1[grep("\\.prof1$",likert_variables1)],
+ post.var=likert_variables1[grep("\\.post1$",likert_variables1)],
+ comm.var=likert_variables1[grep("\\.comm1$",likert_variables1)])
>
>
> get_alpha <- function(data=filtered_conv[filtered$Context == "Italian in Australia",],
+ var=sets$id.var){
+ var_alpha <- alpha(data[,var])
+ dataf <- data.frame(alpha=var_alpha$total,
+ drop = var_alpha$alpha.drop)
+ rownames(dataf) <- rownames(var_alpha$alpha.drop)
+ return(dataf)
+ }
>
> # "Italian in Australia"
> ita_in_au <- do.call(rbind,lapply(sets,function(x) {
+ get_alpha(data=filtered_conv[filtered$Context == "Italian in Australia",],
+ var=x)}))
> ita_in_au$var <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[1])
> ita_in_au$var.full <- sapply(strsplit(rownames(ita_in_au),split="\\."),function(x) x[3])
> ita_in_au$Context <- "Italian in Australia"
> rownames(ita_in_au) <- NULL
>
> # "German in Australia"
> germ_in_au <- do.call(rbind,lapply(sets,function(x) {
+ get_alpha(data=filtered_conv[filtered$Context == "German in Australia",],
+ var=x)}))## Warning in alpha(data[, var]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( knowledge.instru1 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option> germ_in_au$var <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[1])
> germ_in_au$var.full <- sapply(strsplit(rownames(germ_in_au),split="\\."),function(x) x[3])
> germ_in_au$Context <- "German in Australia"
> rownames(germ_in_au) <- NULL
>
> # "English in Germany"
> eng_in_germ <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
+ get_alpha(data=filtered_conv[filtered$Context == "English in Germany",],
+ var=x)}))## Warning in alpha(data[, var]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( people.ought1 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option> # the ones that makes issues
> get_alpha(data=filtered_conv[filtered$Context == "English in Germany",],
+ var=sets$ought.var)## Warning in alpha(data[, var]): Some items were negatively correlated with the total scale and probably
## should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## Some items ( people.ought1 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option## alpha.raw_alpha alpha.std.alpha alpha.G6.smc.
## consider.ought1 0.3811598 0.4843351 0.5179651
## people.ought1 0.3811598 0.4843351 0.5179651
## expect.ought1 0.3811598 0.4843351 0.5179651
## fail.ought1 0.3811598 0.4843351 0.5179651
## alpha.average_r alpha.S.N alpha.ase alpha.mean alpha.sd
## consider.ought1 0.1901594 0.9392439 0.124171 2.193662 0.5264388
## people.ought1 0.1901594 0.9392439 0.124171 2.193662 0.5264388
## expect.ought1 0.1901594 0.9392439 0.124171 2.193662 0.5264388
## fail.ought1 0.1901594 0.9392439 0.124171 2.193662 0.5264388
## drop.raw_alpha drop.std.alpha drop.G6.smc. drop.average_r
## consider.ought1 0.10658753 0.2600925 0.3357223 0.10488382
## people.ought1 0.66493349 0.6896065 0.6118196 0.42547635
## expect.ought1 0.07746381 0.1203628 0.1691397 0.04362117
## fail.ought1 0.32542493 0.4077507 0.3904111 0.18665644
## drop.S.N drop.alpha.se
## consider.ought1 0.3515202 0.19016624
## people.ought1 2.2217171 0.06757193
## expect.ought1 0.1368323 0.18894532
## fail.ought1 0.6884782 0.14195832> eng_in_germ$var <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[1])
> eng_in_germ$var.full <- sapply(strsplit(rownames(eng_in_germ),split="\\."),function(x) x[3])
> eng_in_germ$Context <- "English in Germany"
> rownames(eng_in_germ) <- NULL
>
> # "English in Italy"
> eng_in_ita <- do.call(rbind,lapply(sets[!(names(sets) %in% "comm.var")],function(x) {
+ get_alpha(data=filtered_conv[filtered$Context == "English in Italy",],
+ var=x)}))
> eng_in_ita$var <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[1])
> eng_in_ita$var.full <- sapply(strsplit(rownames(eng_in_ita),split="\\."),function(x) x[3])
> eng_in_ita$Context <- "English in Italy"
> rownames(eng_in_ita) <- NULL
>
> # combine
> full_alpha <- rbind(eng_in_ita,eng_in_germ,germ_in_au,ita_in_au)- Plot alpha by variable
> full_alpha %>% group_by(Context,var) %>%
+ summarise(st.alpha = unique(alpha.std.alpha),
+ G6=unique(alpha.G6.smc.)) %>%
+ ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw()
> all_melt <- all_melt %>% separate(variable,into=c("item","type"),sep="\\.",remove=FALSE)## Warning: Too few values at 652 locations: 9455, 9456, 9457, 9458, 9459,
## 9460, 9461, 9462, 9463, 9464, 9465, 9466, 9467, 9468, 9469, 9470, 9471,
## 9472, 9473, 9474, ...> p1=ggplot(all_melt,aes(x=variable,fill=value)) + geom_bar(position = "stack") +
+ facet_grid(Context~type,scales = "free") + ggtitle("Filtered dataset")+theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8))
>
> p2=ggplot(full_alpha,aes(x=var.full,y=drop.std.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")
>
> p4=ggplot(full_alpha,aes(x=var.full,y=drop.average_r,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme_bw() + facet_wrap(~var,scales="free")
>
> p3=full_alpha %>% group_by(Context,var) %>%
+ summarise(st.alpha = unique(alpha.std.alpha),
+ G6=unique(alpha.G6.smc.)) %>%
+ ggplot(.,aes(x=var,y=st.alpha,colour=Context)) + geom_point() + geom_line(aes(group=Context)) + theme(axis.text.x = element_text(angle = 45, hjust = 1),axis.text=element_text(size=8))
>
>
> plot_grid(p2,p3,p1,p4,nrow=4)